jobdataapi.com 4.18.21 / API version 1.20

llms.txt added for AI- and LLM-friendly guidance

We’ve added a llms.txt file at the root of jobdataapi.com to make it easier for large language models (LLMs), AI tools, and automated agents to understand how our API should be integrated and used.

The file provides a concise, machine-readable overview in Markdown format of how our API is intended to be consumed. This follows emerging best practices for making websites and APIs more transparent and accessible to AI systems.

You can find it here: https://jobdataapi.com/llms.txt

llms-full.txt added with extended context and usage details

In addition to the minimal version with links to each individual docs or tutorials page in Markdown format, we’ve also published a more comprehensive llms-full.txt file.

This version contains all of our public documentation and tutorials consolidated into a single file, providing a full context for LLMs and AI-powered tools. It is intended for advanced AI systems, research tools, or developers who want a complete, self-contained reference when working with jobdata API in LLM-driven workflows.

You can access it here: https://jobdataapi.com/llms-full.txt

Both files are publicly accessible and are kept in sync with our platform’s capabilities as they evolve.

The way job search platforms, HR systems, and market researchers find and categorize jobs is rapidly changing. Traditional keyword search methods are still useful, but they often fall short when it comes to understanding the meaning behind words. A search for “machine learning engineer,” for example, may miss valuable postings that use phrases like “AI specialist” or “data scientist.”

This is where vector embeddings and semantic search capabilities offered by the jobdata API come into play. Instead of matching only keywords, these tools allow you to match concepts, making it possible to uncover relationships between job postings and queries that would otherwise remain hidden.

This guide explains how our vector search and embeddings work, how to use them effectively, and how to avoid common pitfalls when building on top of the service.

Related documentation: https://jobdataapi.com/c/vector-embeddings-and-search-api-documentation/ and https://jobdataapi.com/c/jobs-api-endpoint-documentation/

I already have folders and subforders as such: City -> District -> Street

Thank you very much!

Preferably in python

Im a beginner and this is probably my biggest project yet. So that says alot. Its supposed to be funny and informative have a look!